Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations506
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory55.5 KiB
Average record size in memory112.3 B

Variable types

Numeric13
Categorical1

Alerts

AGE is highly overall correlated with CRIM and 7 other fieldsHigh correlation
CRIM is highly overall correlated with AGE and 8 other fieldsHigh correlation
DIS is highly overall correlated with AGE and 6 other fieldsHigh correlation
INDUS is highly overall correlated with AGE and 7 other fieldsHigh correlation
LSTAT is highly overall correlated with AGE and 7 other fieldsHigh correlation
MEDV is highly overall correlated with AGE and 7 other fieldsHigh correlation
NOX is highly overall correlated with AGE and 8 other fieldsHigh correlation
PTRATIO is highly overall correlated with MEDVHigh correlation
RAD is highly overall correlated with CRIM and 2 other fieldsHigh correlation
RM is highly overall correlated with LSTAT and 1 other fieldsHigh correlation
TAX is highly overall correlated with AGE and 7 other fieldsHigh correlation
ZN is highly overall correlated with AGE and 4 other fieldsHigh correlation
CHAS is highly imbalanced (62.7%) Imbalance
ZN has 360 (71.1%) zeros Zeros

Reproduction

Analysis started2025-01-26 19:58:27.448103
Analysis finished2025-01-26 19:58:47.609393
Duration20.16 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

CRIM
Real number (ℝ)

High correlation 

Distinct406
Distinct (%)80.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9463849
Minimum0.00632
Maximum6.9184395
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-01-27T01:28:47.789643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.00632
5-th percentile0.02791
Q10.083235
median0.29025
Q33.611874
95-th percentile6.9184395
Maximum6.9184395
Range6.9121195
Interquartile range (IQR)3.528639

Descriptive statistics

Standard deviation2.6332452
Coefficient of variation (CV)1.3528903
Kurtosis-0.5606899
Mean1.9463849
Median Absolute Deviation (MAD)0.255695
Skewness1.0694454
Sum984.87075
Variance6.9339804
MonotonicityNot monotonic
2025-01-27T01:28:47.958854image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.9184395 81
 
16.0%
3.611873971 20
 
4.0%
0.01501 2
 
0.4%
0.08829 1
 
0.2%
0.04741 1
 
0.2%
0.00632 1
 
0.2%
0.02731 1
 
0.2%
4.34879 1
 
0.2%
4.03841 1
 
0.2%
3.56868 1
 
0.2%
Other values (396) 396
78.3%
ValueCountFrequency (%)
0.00632 1
0.2%
0.00906 1
0.2%
0.01096 1
0.2%
0.01301 1
0.2%
0.01311 1
0.2%
0.0136 1
0.2%
0.01381 1
0.2%
0.01432 1
0.2%
0.01439 1
0.2%
0.01501 2
0.4%
ValueCountFrequency (%)
6.9184395 81
16.0%
6.80117 1
 
0.2%
6.71772 1
 
0.2%
6.65492 1
 
0.2%
6.53876 1
 
0.2%
6.44405 1
 
0.2%
6.39312 1
 
0.2%
6.28807 1
 
0.2%
5.87205 1
 
0.2%
5.82401 1
 
0.2%

ZN
Real number (ℝ)

High correlation  Zeros 

Distinct11
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5232559
Minimum0
Maximum28.029835
Zeros360
Zeros (%)71.1%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-01-27T01:28:48.102682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q311.211934
95-th percentile28.029835
Maximum28.029835
Range28.029835
Interquartile range (IQR)11.211934

Descriptive statistics

Standard deviation10.810961
Coefficient of variation (CV)1.6572952
Kurtosis-0.34015755
Mean6.5232559
Median Absolute Deviation (MAD)0
Skewness1.2029836
Sum3300.7675
Variance116.87687
MonotonicityNot monotonic
2025-01-27T01:28:48.233720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 360
71.1%
28.029835 68
 
13.4%
11.21193416 20
 
4.0%
20 20
 
4.0%
12.5 10
 
2.0%
22 10
 
2.0%
25 10
 
2.0%
21 4
 
0.8%
28 2
 
0.4%
18 1
 
0.2%
ValueCountFrequency (%)
0 360
71.1%
11.21193416 20
 
4.0%
12.5 10
 
2.0%
17.5 1
 
0.2%
18 1
 
0.2%
20 20
 
4.0%
21 4
 
0.8%
22 10
 
2.0%
25 10
 
2.0%
28 2
 
0.4%
ValueCountFrequency (%)
28.029835 68
13.4%
28 2
 
0.4%
25 10
 
2.0%
22 10
 
2.0%
21 4
 
0.8%
20 20
 
4.0%
18 1
 
0.2%
17.5 1
 
0.2%
12.5 10
 
2.0%
11.21193416 20
 
4.0%

INDUS
Real number (ℝ)

High correlation 

Distinct77
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.083992
Minimum0.46
Maximum27.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-01-27T01:28:48.369346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.46
5-th percentile2.18
Q15.19
median9.9
Q318.1
95-th percentile19.58
Maximum27.74
Range27.28
Interquartile range (IQR)12.91

Descriptive statistics

Standard deviation6.6991648
Coefficient of variation (CV)0.60440001
Kurtosis-1.1439136
Mean11.083992
Median Absolute Deviation (MAD)6.46
Skewness0.30987063
Sum5608.4998
Variance44.878808
MonotonicityNot monotonic
2025-01-27T01:28:48.517891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.1 127
25.1%
19.58 28
 
5.5%
8.14 22
 
4.3%
11.08399177 20
 
4.0%
6.2 18
 
3.6%
21.89 14
 
2.8%
3.97 12
 
2.4%
9.9 12
 
2.4%
10.59 11
 
2.2%
8.56 11
 
2.2%
Other values (67) 231
45.7%
ValueCountFrequency (%)
0.46 1
 
0.2%
0.74 1
 
0.2%
1.21 1
 
0.2%
1.22 1
 
0.2%
1.25 2
0.4%
1.32 1
 
0.2%
1.38 1
 
0.2%
1.47 2
0.4%
1.52 4
0.8%
1.69 2
0.4%
ValueCountFrequency (%)
27.74 5
 
1.0%
25.65 6
 
1.2%
21.89 14
 
2.8%
19.58 28
 
5.5%
18.1 127
25.1%
15.04 3
 
0.6%
13.92 4
 
0.8%
13.89 3
 
0.6%
12.83 6
 
1.2%
11.93 5
 
1.0%

CHAS
Categorical

Imbalance 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
0.0
452 
1.0
 
34
0.06995884773662552
 
20

Length

Max length19
Median length3
Mean length3.6324111
Min length3

Characters and Unicode

Total characters1838
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 452
89.3%
1.0 34
 
6.7%
0.06995884773662552 20
 
4.0%

Length

2025-01-27T01:28:48.671650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T01:28:48.798670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 452
89.3%
1.0 34
 
6.7%
0.06995884773662552 20
 
4.0%

Most occurring characters

ValueCountFrequency (%)
0 978
53.2%
. 506
27.5%
6 60
 
3.3%
5 60
 
3.3%
7 40
 
2.2%
9 40
 
2.2%
8 40
 
2.2%
2 40
 
2.2%
1 34
 
1.8%
4 20
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1838
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 978
53.2%
. 506
27.5%
6 60
 
3.3%
5 60
 
3.3%
7 40
 
2.2%
9 40
 
2.2%
8 40
 
2.2%
2 40
 
2.2%
1 34
 
1.8%
4 20
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1838
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 978
53.2%
. 506
27.5%
6 60
 
3.3%
5 60
 
3.3%
7 40
 
2.2%
9 40
 
2.2%
8 40
 
2.2%
2 40
 
2.2%
1 34
 
1.8%
4 20
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1838
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 978
53.2%
. 506
27.5%
6 60
 
3.3%
5 60
 
3.3%
7 40
 
2.2%
9 40
 
2.2%
8 40
 
2.2%
2 40
 
2.2%
1 34
 
1.8%
4 20
 
1.1%

NOX
Real number (ℝ)

High correlation 

Distinct81
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.55469506
Minimum0.385
Maximum0.871
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-01-27T01:28:48.929904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.385
5-th percentile0.40925
Q10.449
median0.538
Q30.624
95-th percentile0.74
Maximum0.871
Range0.486
Interquartile range (IQR)0.175

Descriptive statistics

Standard deviation0.11587768
Coefficient of variation (CV)0.20890339
Kurtosis-0.064667133
Mean0.55469506
Median Absolute Deviation (MAD)0.0875
Skewness0.72930792
Sum280.6757
Variance0.013427636
MonotonicityNot monotonic
2025-01-27T01:28:49.153745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.538 23
 
4.5%
0.713 18
 
3.6%
0.437 17
 
3.4%
0.871 16
 
3.2%
0.624 15
 
3.0%
0.489 15
 
3.0%
0.693 14
 
2.8%
0.605 14
 
2.8%
0.74 13
 
2.6%
0.544 12
 
2.4%
Other values (71) 349
69.0%
ValueCountFrequency (%)
0.385 1
 
0.2%
0.389 1
 
0.2%
0.392 2
0.4%
0.394 1
 
0.2%
0.398 2
0.4%
0.4 4
0.8%
0.401 3
0.6%
0.403 3
0.6%
0.404 3
0.6%
0.405 3
0.6%
ValueCountFrequency (%)
0.871 16
3.2%
0.77 8
1.6%
0.74 13
2.6%
0.718 6
 
1.2%
0.713 18
3.6%
0.7 11
2.2%
0.693 14
2.8%
0.679 8
1.6%
0.671 7
 
1.4%
0.668 3
 
0.6%

RM
Real number (ℝ)

High correlation 

Distinct446
Distinct (%)88.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2846344
Minimum3.561
Maximum8.78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-01-27T01:28:49.301766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3.561
5-th percentile5.314
Q15.8855
median6.2085
Q36.6235
95-th percentile7.5875
Maximum8.78
Range5.219
Interquartile range (IQR)0.738

Descriptive statistics

Standard deviation0.70261714
Coefficient of variation (CV)0.11179921
Kurtosis1.8915004
Mean6.2846344
Median Absolute Deviation (MAD)0.3455
Skewness0.40361213
Sum3180.025
Variance0.49367085
MonotonicityNot monotonic
2025-01-27T01:28:49.448576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.229 3
 
0.6%
6.127 3
 
0.6%
5.713 3
 
0.6%
6.417 3
 
0.6%
6.405 3
 
0.6%
6.167 3
 
0.6%
5.304 2
 
0.4%
5.39 2
 
0.4%
6.193 2
 
0.4%
4.138 2
 
0.4%
Other values (436) 480
94.9%
ValueCountFrequency (%)
3.561 1
0.2%
3.863 1
0.2%
4.138 2
0.4%
4.368 1
0.2%
4.519 1
0.2%
4.628 1
0.2%
4.652 1
0.2%
4.88 1
0.2%
4.903 1
0.2%
4.906 1
0.2%
ValueCountFrequency (%)
8.78 1
0.2%
8.725 1
0.2%
8.704 1
0.2%
8.398 1
0.2%
8.375 1
0.2%
8.337 1
0.2%
8.297 1
0.2%
8.266 1
0.2%
8.259 1
0.2%
8.247 1
0.2%

AGE
Real number (ℝ)

High correlation 

Distinct349
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.518519
Minimum2.9
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-01-27T01:28:49.594260image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.9
5-th percentile18.4
Q145.925
median74.45
Q393.575
95-th percentile100
Maximum100
Range97.1
Interquartile range (IQR)47.65

Descriptive statistics

Standard deviation27.439466
Coefficient of variation (CV)0.40046788
Kurtosis-0.89845748
Mean68.518519
Median Absolute Deviation (MAD)20.9
Skewness-0.59426137
Sum34670.37
Variance752.9243
MonotonicityNot monotonic
2025-01-27T01:28:49.743634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 42
 
8.3%
68.51851852 20
 
4.0%
97.9 4
 
0.8%
96 4
 
0.8%
87.9 4
 
0.8%
98.8 4
 
0.8%
95.4 4
 
0.8%
76.5 3
 
0.6%
32.2 3
 
0.6%
36.6 3
 
0.6%
Other values (339) 415
82.0%
ValueCountFrequency (%)
2.9 1
0.2%
6.2 1
0.2%
6.5 1
0.2%
6.6 2
0.4%
6.8 1
0.2%
7.8 2
0.4%
8.4 1
0.2%
8.9 1
0.2%
9.8 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
100 42
8.3%
99.3 1
 
0.2%
99.1 1
 
0.2%
98.9 3
 
0.6%
98.8 4
 
0.8%
98.7 1
 
0.2%
98.5 1
 
0.2%
98.4 2
 
0.4%
98.3 2
 
0.4%
98.2 2
 
0.4%

DIS
Real number (ℝ)

High correlation 

Distinct412
Distinct (%)81.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7950427
Minimum1.1296
Maximum12.1265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-01-27T01:28:49.887426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.1296
5-th percentile1.461975
Q12.100175
median3.20745
Q35.188425
95-th percentile7.8278
Maximum12.1265
Range10.9969
Interquartile range (IQR)3.08825

Descriptive statistics

Standard deviation2.1057101
Coefficient of variation (CV)0.55485809
Kurtosis0.48794112
Mean3.7950427
Median Absolute Deviation (MAD)1.29115
Skewness1.0117806
Sum1920.2916
Variance4.4340151
MonotonicityNot monotonic
2025-01-27T01:28:50.029862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.4952 5
 
1.0%
5.4007 4
 
0.8%
5.2873 4
 
0.8%
5.7209 4
 
0.8%
6.8147 4
 
0.8%
6.0622 3
 
0.6%
6.4798 3
 
0.6%
3.6519 3
 
0.6%
7.309 3
 
0.6%
6.498 3
 
0.6%
Other values (402) 470
92.9%
ValueCountFrequency (%)
1.1296 1
0.2%
1.137 1
0.2%
1.1691 1
0.2%
1.1742 1
0.2%
1.1781 1
0.2%
1.2024 1
0.2%
1.2852 1
0.2%
1.3163 1
0.2%
1.3216 1
0.2%
1.3325 1
0.2%
ValueCountFrequency (%)
12.1265 1
0.2%
10.7103 2
0.4%
10.5857 2
0.4%
9.2229 1
0.2%
9.2203 2
0.4%
9.1876 1
0.2%
9.0892 1
0.2%
8.9067 2
0.4%
8.7921 2
0.4%
8.6966 1
0.2%

RAD
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5494071
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-01-27T01:28:50.147831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q324
95-th percentile24
Maximum24
Range23
Interquartile range (IQR)20

Descriptive statistics

Standard deviation8.7072594
Coefficient of variation (CV)0.91181152
Kurtosis-0.86723199
Mean9.5494071
Median Absolute Deviation (MAD)2
Skewness1.0048146
Sum4832
Variance75.816366
MonotonicityNot monotonic
2025-01-27T01:28:50.260396image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
24 132
26.1%
5 115
22.7%
4 110
21.7%
3 38
 
7.5%
6 26
 
5.1%
8 24
 
4.7%
2 24
 
4.7%
1 20
 
4.0%
7 17
 
3.4%
ValueCountFrequency (%)
1 20
 
4.0%
2 24
 
4.7%
3 38
 
7.5%
4 110
21.7%
5 115
22.7%
6 26
 
5.1%
7 17
 
3.4%
8 24
 
4.7%
24 132
26.1%
ValueCountFrequency (%)
24 132
26.1%
8 24
 
4.7%
7 17
 
3.4%
6 26
 
5.1%
5 115
22.7%
4 110
21.7%
3 38
 
7.5%
2 24
 
4.7%
1 20
 
4.0%

TAX
Real number (ℝ)

High correlation 

Distinct66
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean408.23715
Minimum187
Maximum711
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-01-27T01:28:50.392030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum187
5-th percentile222
Q1279
median330
Q3666
95-th percentile666
Maximum711
Range524
Interquartile range (IQR)387

Descriptive statistics

Standard deviation168.53712
Coefficient of variation (CV)0.4128412
Kurtosis-1.142408
Mean408.23715
Median Absolute Deviation (MAD)73
Skewness0.66995594
Sum206568
Variance28404.759
MonotonicityNot monotonic
2025-01-27T01:28:50.537539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
666 132
26.1%
307 40
 
7.9%
403 30
 
5.9%
437 15
 
3.0%
304 14
 
2.8%
264 12
 
2.4%
398 12
 
2.4%
384 11
 
2.2%
277 11
 
2.2%
330 10
 
2.0%
Other values (56) 219
43.3%
ValueCountFrequency (%)
187 1
 
0.2%
188 7
1.4%
193 8
1.6%
198 1
 
0.2%
216 5
1.0%
222 7
1.4%
223 5
1.0%
224 10
2.0%
226 1
 
0.2%
233 9
1.8%
ValueCountFrequency (%)
711 5
 
1.0%
666 132
26.1%
469 1
 
0.2%
437 15
 
3.0%
432 9
 
1.8%
430 3
 
0.6%
422 1
 
0.2%
411 2
 
0.4%
403 30
 
5.9%
402 2
 
0.4%

PTRATIO
Real number (ℝ)

High correlation 

Distinct46
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.455534
Minimum12.6
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-01-27T01:28:50.676394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum12.6
5-th percentile14.7
Q117.4
median19.05
Q320.2
95-th percentile21
Maximum22
Range9.4
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation2.1649455
Coefficient of variation (CV)0.11730604
Kurtosis-0.28509138
Mean18.455534
Median Absolute Deviation (MAD)1.15
Skewness-0.80232493
Sum9338.5
Variance4.6869891
MonotonicityNot monotonic
2025-01-27T01:28:50.817818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
20.2 140
27.7%
14.7 34
 
6.7%
21 27
 
5.3%
17.8 23
 
4.5%
19.2 19
 
3.8%
17.4 18
 
3.6%
19.1 17
 
3.4%
18.6 17
 
3.4%
18.4 16
 
3.2%
16.6 16
 
3.2%
Other values (36) 179
35.4%
ValueCountFrequency (%)
12.6 3
 
0.6%
13 12
 
2.4%
13.6 1
 
0.2%
14.4 1
 
0.2%
14.7 34
6.7%
14.8 3
 
0.6%
14.9 4
 
0.8%
15.1 1
 
0.2%
15.2 13
 
2.6%
15.3 3
 
0.6%
ValueCountFrequency (%)
22 2
 
0.4%
21.2 15
 
3.0%
21.1 1
 
0.2%
21 27
 
5.3%
20.9 11
 
2.2%
20.2 140
27.7%
20.1 5
 
1.0%
19.7 8
 
1.6%
19.6 8
 
1.6%
19.2 19
 
3.8%

B
Real number (ℝ)

Distinct282
Distinct (%)55.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean381.91884
Minimum344.10625
Maximum396.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-01-27T01:28:50.952682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum344.10625
5-th percentile344.10625
Q1375.3775
median391.44
Q3396.225
95-th percentile396.9
Maximum396.9
Range52.79375
Interquartile range (IQR)20.8475

Descriptive statistics

Standard deviation19.054913
Coefficient of variation (CV)0.049892571
Kurtosis-0.23058954
Mean381.91884
Median Absolute Deviation (MAD)5.46
Skewness-1.1642076
Sum193250.93
Variance363.0897
MonotonicityNot monotonic
2025-01-27T01:28:51.106346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
396.9 121
23.9%
344.10625 77
 
15.2%
395.24 3
 
0.6%
393.74 3
 
0.6%
389.71 2
 
0.4%
394.12 2
 
0.4%
395.6 2
 
0.4%
388.45 2
 
0.4%
392.78 2
 
0.4%
395.11 2
 
0.4%
Other values (272) 290
57.3%
ValueCountFrequency (%)
344.10625 77
15.2%
344.91 1
 
0.2%
347.88 1
 
0.2%
348.13 1
 
0.2%
348.93 1
 
0.2%
349.48 1
 
0.2%
350.45 1
 
0.2%
350.65 1
 
0.2%
351.85 1
 
0.2%
352.58 1
 
0.2%
ValueCountFrequency (%)
396.9 121
23.9%
396.42 1
 
0.2%
396.33 1
 
0.2%
396.3 1
 
0.2%
396.28 1
 
0.2%
396.24 1
 
0.2%
396.23 1
 
0.2%
396.21 2
 
0.4%
396.14 1
 
0.2%
396.06 2
 
0.4%

LSTAT
Real number (ℝ)

High correlation 

Distinct439
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.715432
Minimum1.73
Maximum37.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-01-27T01:28:51.249232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.73
5-th percentile3.7375
Q17.23
median11.995
Q316.57
95-th percentile26.8075
Maximum37.97
Range36.24
Interquartile range (IQR)9.34

Descriptive statistics

Standard deviation7.0127389
Coefficient of variation (CV)0.55151401
Kurtosis0.6634895
Mean12.715432
Median Absolute Deviation (MAD)4.695
Skewness0.92729111
Sum6434.0086
Variance49.178507
MonotonicityNot monotonic
2025-01-27T01:28:51.399698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.7154321 20
 
4.0%
8.05 3
 
0.6%
14.1 3
 
0.6%
6.36 3
 
0.6%
7.79 3
 
0.6%
18.13 3
 
0.6%
3.11 2
 
0.4%
4.56 2
 
0.4%
6.72 2
 
0.4%
7.6 2
 
0.4%
Other values (429) 463
91.5%
ValueCountFrequency (%)
1.73 1
0.2%
1.92 1
0.2%
1.98 1
0.2%
2.47 1
0.2%
2.87 1
0.2%
2.88 1
0.2%
2.94 1
0.2%
2.96 1
0.2%
2.97 1
0.2%
2.98 1
0.2%
ValueCountFrequency (%)
37.97 1
0.2%
36.98 1
0.2%
34.77 1
0.2%
34.41 1
0.2%
34.37 1
0.2%
34.02 1
0.2%
31.99 1
0.2%
30.81 2
0.4%
30.63 1
0.2%
30.62 1
0.2%

MEDV
Real number (ℝ)

High correlation 

Distinct229
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.532806
Minimum5
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-01-27T01:28:51.562220image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile10.2
Q117.025
median21.2
Q325
95-th percentile43.4
Maximum50
Range45
Interquartile range (IQR)7.975

Descriptive statistics

Standard deviation9.1971041
Coefficient of variation (CV)0.40816505
Kurtosis1.4951969
Mean22.532806
Median Absolute Deviation (MAD)4
Skewness1.1080984
Sum11401.6
Variance84.586724
MonotonicityNot monotonic
2025-01-27T01:28:51.710415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 16
 
3.2%
25 8
 
1.6%
23.1 7
 
1.4%
22 7
 
1.4%
21.7 7
 
1.4%
20.6 6
 
1.2%
19.4 6
 
1.2%
22.6 5
 
1.0%
21.4 5
 
1.0%
21.2 5
 
1.0%
Other values (219) 434
85.8%
ValueCountFrequency (%)
5 2
0.4%
5.6 1
 
0.2%
6.3 1
 
0.2%
7 2
0.4%
7.2 3
0.6%
7.4 1
 
0.2%
7.5 1
 
0.2%
8.1 1
 
0.2%
8.3 2
0.4%
8.4 2
0.4%
ValueCountFrequency (%)
50 16
3.2%
48.8 1
 
0.2%
48.5 1
 
0.2%
48.3 1
 
0.2%
46.7 1
 
0.2%
46 1
 
0.2%
45.4 1
 
0.2%
44.8 1
 
0.2%
44 1
 
0.2%
43.8 1
 
0.2%

Interactions

2025-01-27T01:28:46.057801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:27.982031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:29.849245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:31.717185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:33.517201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:35.787182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:37.027128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:38.293222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:39.497171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:40.769798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:41.997248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:43.183393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:44.800392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:46.153392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:28.155481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:29.996983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:31.869307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:33.680242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:35.900051image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:37.130548image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:38.397558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:39.607291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:40.873806image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:42.095866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:43.280475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:44.907745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:46.243358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:28.300812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:30.138117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:32.001008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:33.827424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:36.001070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:37.219324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:38.487440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:39.708387image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:40.967622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:42.190467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:43.372235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:45.001460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:46.333976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:28.422794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:30.286943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:32.138879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:33.964924image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:36.104672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:37.328702image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:38.580432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:39.819576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:41.061420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:42.279458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:43.459463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:45.101373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:46.423823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:28.554895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:30.433870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:32.268219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:34.098444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:36.197674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:37.441455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:38.670561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:39.914728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:41.157180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:42.369240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:43.981698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:45.194127image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:46.513905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:28.694607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:30.601183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:32.403561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:34.242112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:36.291697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:37.548499image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:38.759502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:40.003594image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:41.251403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:42.459473image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:44.067517image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:45.294987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:46.605560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:28.835093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:30.759235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:32.537176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:34.367001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:36.381095image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:37.643512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:38.849339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:40.098283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:41.351298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:42.550497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:44.162157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:45.390069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:46.695674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:28.976571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:30.898576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:32.675080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:34.495193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:36.469465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:37.738303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:38.942882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:40.205657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:41.442652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:42.641819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:44.269353image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:45.487277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:46.784580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:29.123730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:31.033567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:32.812345image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:34.627318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:36.569864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:37.834706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:39.037544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:40.301010image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:41.531737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:42.731256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:44.352096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:45.586166image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:46.887458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:29.277197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:31.192289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:32.956264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:34.763514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:36.661438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:37.927202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:39.132096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:40.399467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:41.623008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:42.823453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:44.446525image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:45.682116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:46.970632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:29.420903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:31.322267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:33.100968image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:34.895425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:36.751838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:38.017218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:39.226620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:40.489511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:41.713367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:42.910633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:44.530027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:45.771555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:47.055393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:29.559355image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:31.448385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:33.241476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:35.031502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:36.840469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:38.108462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:39.309470image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:40.584749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:41.804953image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:42.993643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:44.617548image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:45.867290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:47.147747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:29.712574image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:31.595390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:33.388569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:35.687417image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:36.941298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:38.207476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:39.407180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:40.678564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:41.900559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:43.093534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:44.717074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T01:28:45.967342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-01-27T01:28:51.832294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AGEBCHASCRIMDISINDUSLSTATMEDVNOXPTRATIORADRMTAXZN
AGE1.000-0.2220.0000.661-0.7790.6460.630-0.5430.7740.3550.413-0.2710.518-0.510
B-0.2221.0000.000-0.3420.249-0.277-0.2040.179-0.295-0.065-0.2750.060-0.3260.142
CHAS0.0000.0001.0000.1140.0370.0800.0000.1400.0950.0880.1020.0960.0280.000
CRIM0.661-0.3420.1141.000-0.7070.6860.588-0.5310.7900.4330.732-0.2840.724-0.502
DIS-0.7790.2490.037-0.7071.000-0.751-0.5490.446-0.880-0.322-0.4960.263-0.5740.586
INDUS0.646-0.2770.0800.686-0.7511.0000.606-0.5680.7760.4290.470-0.3970.662-0.600
LSTAT0.630-0.2040.0000.588-0.5490.6061.000-0.8310.6200.4660.376-0.6210.520-0.470
MEDV-0.5430.1790.140-0.5310.446-0.568-0.8311.000-0.563-0.556-0.3470.634-0.5620.430
NOX0.774-0.2950.0950.790-0.8800.7760.620-0.5631.0000.3910.586-0.3100.650-0.605
PTRATIO0.355-0.0650.0880.433-0.3220.4290.466-0.5560.3911.0000.318-0.3130.453-0.458
RAD0.413-0.2750.1020.732-0.4960.4700.376-0.3470.5860.3181.000-0.1070.705-0.265
RM-0.2710.0600.096-0.2840.263-0.397-0.6210.634-0.310-0.313-0.1071.000-0.2720.353
TAX0.518-0.3260.0280.724-0.5740.6620.520-0.5620.6500.4530.705-0.2721.000-0.362
ZN-0.5100.1420.000-0.5020.586-0.600-0.4700.430-0.605-0.458-0.2650.353-0.3621.000

Missing values

2025-01-27T01:28:47.283951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-27T01:28:47.499077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTATMEDV
00.0063218.02.310.0000000.5386.57565.24.0900129615.3396.904.98000024.0
10.027310.07.070.0000000.4696.42178.94.9671224217.8396.909.14000021.6
20.027290.07.070.0000000.4697.18561.14.9671224217.8392.834.03000034.7
30.032370.02.180.0000000.4586.99845.86.0622322218.7394.632.94000033.4
40.069050.02.180.0000000.4587.14754.26.0622322218.7396.9012.71543236.2
50.029850.02.180.0000000.4586.43058.76.0622322218.7394.125.21000028.7
60.0882912.57.870.0699590.5246.01266.65.5605531115.2395.6012.43000022.9
70.1445512.57.870.0000000.5246.17296.15.9505531115.2396.9019.15000027.1
80.2112412.57.870.0000000.5245.631100.06.0821531115.2386.6329.93000016.5
90.1700412.57.870.0699590.5246.00485.96.5921531115.2386.7117.10000018.9
CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTATMEDV
4960.289600.09.690.00.5855.39072.9000002.7986639119.2396.9021.14000019.7
4970.268380.09.690.00.5855.79470.6000002.8927639119.2396.9014.10000018.3
4980.239120.09.690.00.5856.01965.3000002.4091639119.2396.9012.92000021.2
4990.177830.09.690.00.5855.56973.5000002.3999639119.2395.7715.10000017.5
5000.224380.09.690.00.5856.02779.7000002.4982639119.2396.9014.33000016.8
5010.062630.011.930.00.5736.59369.1000002.4786127321.0391.9912.71543222.4
5020.045270.011.930.00.5736.12076.7000002.2875127321.0396.909.08000020.6
5030.060760.011.930.00.5736.97691.0000002.1675127321.0396.905.64000023.9
5040.109590.011.930.00.5736.79489.3000002.3889127321.0393.456.48000022.0
5050.047410.011.930.00.5736.03068.5185192.5050127321.0396.907.88000011.9